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parametersSetup.py
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Fri, May 24, 13:53

parametersSetup.py
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#!~/anaconda3/bin python
__author__ = "Una Pale"
__credits__ = ["Una Pale", "Renato Zanetti"]
__license__ = "LGPL"
__version__ = "1.0"
__email__ = "una.pale at epfl.ch"
''' file with all parameters'''
import numpy as np
import pickle
class GeneralParams:
#Label smoothing with moving average
seizureStableLenToTest=5 #in seconds - window for performing label voting
seizureStablePercToTest=0.5 # 50% of 1 in last seizureStableLenToTest values that needs to be 1 to finally keep label 1
distanceBetween2Seizures=30 #in seconds - if seizures are closer then this then they are merged
timeBeforeSeizureConsideredAsSeizure=30 #in seconds - if seizure starts bit before true seizure to still consider ok
numFPperDayThr=1 #for additional measure of performance what number of FP seizures per days we consider ok
#Label smoothing with bayes
bayesWind=10 #orignal paper uses 5 windows
bayesProbThresh= 1.5 #smoothing with cummulative probabilities, threshold from Valentins paper
#tolerance for FP before and after seizure not to considered as FP
toleranceFP_befSeiz=10 #in sec
toleranceFP_aftSeiz=30 #in sec
patients=[] #on which subjects to train and test
plottingON=0 #determines whether some additional plots are plotted
PersGenApproach='personalized' #'personalized', 'generalized' approaches - generalized not used here
class SigInfoParams:
#CHB-MIT: common channels among all subjects
channels = ['FP1-F7', 'F7-T7', 'T7-P7', 'P7-O1', 'FP1-F3', 'F3-C3', 'C3-P3', 'P3-O1',
'FP2-F4', 'F4-C4', 'C4-P4', 'P4-O2', 'FP2-F8', 'F8-T8', 'T8-P8', 'P8-O2', 'FZ-CZ', 'CZ-PZ']
chToKeep=np.array([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]) #which channels to keep - after reordering them in above order
samplFreq=256 #sampling frequency of data
class SegSymbParams:
# window discretization
segLenSec=4 #length of discrete EEG windows on which to perform analysis
slidWindStepSec=0.5 #step of sliding window
labelVotingType='majority' #'majority', 'atLeastOne' or 'allOne' #defines how final label of a segment is chosen
class StandardMLParams:
modelType='DecisionTree' #'KNN', 'SVM', 'DecisionTree', 'RandomForest','BaggingClassifier','AdaBoost'
trainingDataResampling='NoResampling' #'NoResampling','ROS','RUS','TomekLinks','ClusterCentroids','SMOTE','SMOTEtomek'
samplingStrategy='default' # depends on resampling, but if 'default' then default for each resampling type, otherwise now implemented only for RUS if not default
#KNN parameters
KNN_n_neighbors=5
KNN_metric='euclidean' #'euclidean', 'manhattan'
#SVM parameters
SVM_kernel = 'linear' # 'linear', 'rbf','poly'
SVM_C = 1 # 1,100,1000
SVM_gamma = 'auto' # 0 # 0,10,100
#DecisionTree and random forest parameters
DecisionTree_criterion = 'gini' # 'gini', 'entropy'
DecisionTree_splitter = 'best' # 'best','random'
DecisionTree_max_depth = 0 # 0, 2, 5,10,20
RandomForest_n_estimators = 100 #10,50, 100,250
#Bagging, boosting classifier parameters
Bagging_base_estimator='SVM' #'SVM','KNN', 'DecisionTree'
Bagging_n_estimators = 100 # 10,50, 100,250
n_jobs = 1
class FeaturesParams:
numStandardFeat=62 #56 features from Sopic2018 + 6 AZC features
featNorm='noNorm' #'Norm','noNorm' #feature normalization flag
class ZeroCrossFeatureParams:
EPS_thresh_arr = [16, 32, 64, 128, 256] #thresholds for AZC calculation
buttFilt_order=4 #Butterworth filter order
buttFilt_lfreq = 1 #low freq
buttFilt_hfreq=20 #high freq
samplFreq=256 #expected sampling frequency for the input signal
winLen=4 # data window length in sec
winStep=0.5 # non-overlapping data in sec
#SAVING SETUP once again to update if new info
with open('../PARAMETERS.pickle', 'wb') as f:
# pickle.dump([GeneralParams, SegSymbParams, SigInfoParams, EEGfreqBands, StandardMLParams, FeaturesParams, ZeroCrossFeatureParams, patients], f)
pickle.dump([GeneralParams, SegSymbParams, SigInfoParams, StandardMLParams, FeaturesParams, ZeroCrossFeatureParams], f)

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